Overview

Dataset statistics

Number of variables23
Number of observations670
Missing cells5
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory189.5 KiB
Average record size in memory289.6 B

Variable types

NUM20
CAT2
DATE1

Reproduction

Analysis started2020-11-27 17:37:10.105039
Analysis finished2020-11-27 17:38:17.002044
Duration1 minute and 6.9 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

china_pm2.5 has a high cardinality: 163 distinct values High cardinality
seoul_covid_death is highly correlated with seoul_covid_confirmed and 1 other fieldsHigh correlation
seoul_covid_confirmed is highly correlated with seoul_covid_death and 1 other fieldsHigh correlation
seoul_covid_released is highly correlated with seoul_covid_confirmed and 1 other fieldsHigh correlation
y_pm10 is highly correlated with y_pm2.5High correlation
y_pm2.5 is highly correlated with y_pm10High correlation
transport_out is highly correlated with transport_in and 1 other fieldsHigh correlation
transport_in is highly correlated with transport_out and 1 other fieldsHigh correlation
transport_inandout is highly correlated with transport_in and 1 other fieldsHigh correlation
기압 is highly skewed (γ1 = -24.34635554) Skewed
안개시간 is highly skewed (γ1 = 21.71745376) Skewed
china_covid_death is highly skewed (γ1 = 20.48147298) Skewed
date has unique values Unique
transport_out has unique values Unique
transport_inandout has unique values Unique
seoul_covid_confirmed has 412 (61.5%) zeros Zeros
seoul_covid_death has 463 (69.1%) zeros Zeros
seoul_covid_released has 412 (61.5%) zeros Zeros
강수량 has 479 (71.5%) zeros Zeros
안개시간 has 665 (99.3%) zeros Zeros
china_covid_confirmed has 381 (56.9%) zeros Zeros
china_covid_death has 530 (79.1%) zeros Zeros

Variables

date
Date

UNIQUE

Distinct count670
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Minimum2019-01-01 00:00:00
Maximum2020-10-31 00:00:00
2020-11-28T02:38:17.176578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:17.420924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram

seoul_covid_confirmed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count246
Unique (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean769.8134328358209
Minimum0.0
Maximum6011.0
Zeros412
Zeros (%)61.5%
Memory size5.4 KiB
2020-11-28T02:38:17.922669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3733.75
95-th percentile5222.45
Maximum6011
Range6011
Interquartile range (IQR)733.75

Descriptive statistics

Standard deviation1538.826284
Coefficient of variation (CV)1.998960031
Kurtosis3.991387147
Mean769.8134328
Median Absolute Deviation (MAD)0
Skewness2.276549968
Sum515775
Variance2367986.331
2020-11-28T02:38:18.014461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
041261.5%
 
63760.9%
 
63330.4%
 
62930.4%
 
62830.4%
 
1420.3%
 
62420.3%
 
166210.1%
 
123010.1%
 
80210.1%
 
566810.1%
 
77410.1%
 
125010.1%
 
63410.1%
 
74210.1%
 
69510.1%
 
60210.1%
 
167510.1%
 
59010.1%
 
26510.1%
 
147410.1%
 
207710.1%
 
133410.1%
 
585110.1%
 
490410.1%
 
Other values (221)22133.0%
 
ValueCountFrequency (%) 
041261.5%
 
1420.3%
 
1510.1%
 
2210.1%
 
2710.1%
 
3010.1%
 
3110.1%
 
3510.1%
 
4110.1%
 
4910.1%
 
ValueCountFrequency (%) 
601110.1%
 
596010.1%
 
591210.1%
 
587610.1%
 
585110.1%
 
582710.1%
 
580710.1%
 
579010.1%
 
576810.1%
 
574810.1%
 

seoul_covid_death
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count58
Unique (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.477611940298507
Minimum0.0
Maximum78.0
Zeros463
Zeros (%)69.1%
Memory size5.4 KiB
2020-11-28T02:38:18.103228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile53.55
Maximum78
Range78
Interquartile range (IQR)4

Descriptive statistics

Standard deviation16.29663203
Coefficient of variation (CV)2.515839507
Kurtosis8.255384787
Mean6.47761194
Median Absolute Deviation (MAD)0
Skewness3.020514403
Sum4340
Variance265.5802155
2020-11-28T02:38:18.199171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
046369.1%
 
2355.2%
 
4345.1%
 
11142.1%
 
13111.6%
 
9101.5%
 
791.3%
 
671.0%
 
7450.7%
 
850.7%
 
1650.7%
 
3230.4%
 
1030.4%
 
2430.4%
 
7830.4%
 
6130.4%
 
6930.4%
 
4620.3%
 
6220.3%
 
2320.3%
 
5720.3%
 
1420.3%
 
1220.3%
 
6420.3%
 
6620.3%
 
Other values (33)385.7%
 
ValueCountFrequency (%) 
046369.1%
 
2355.2%
 
310.1%
 
4345.1%
 
510.1%
 
671.0%
 
791.3%
 
850.7%
 
9101.5%
 
1030.4%
 
ValueCountFrequency (%) 
7830.4%
 
7710.1%
 
7610.1%
 
7450.7%
 
7110.1%
 
6930.4%
 
6810.1%
 
6710.1%
 
6620.3%
 
6510.1%
 

seoul_covid_released
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count230
Unique (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean586.9104477611941
Minimum0.0
Maximum5495.0
Zeros412
Zeros (%)61.5%
Memory size5.4 KiB
2020-11-28T02:38:18.300903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3574.75
95-th percentile4328.2
Maximum5495
Range5495
Interquartile range (IQR)574.75

Descriptive statistics

Standard deviation1264.400424
Coefficient of variation (CV)2.154332792
Kurtosis5.968081342
Mean586.9104478
Median Absolute Deviation (MAD)0
Skewness2.602781161
Sum393230
Variance1598708.431
2020-11-28T02:38:18.392203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
041261.5%
 
6440.6%
 
1340.6%
 
630.4%
 
830.4%
 
62730.4%
 
163930.4%
 
157130.4%
 
60420.3%
 
11720.3%
 
7220.3%
 
5520.3%
 
65120.3%
 
9320.3%
 
68920.3%
 
73320.3%
 
535020.3%
 
2520.3%
 
920.3%
 
520.3%
 
320.3%
 
39310.1%
 
433010.1%
 
489410.1%
 
496510.1%
 
Other values (205)20530.6%
 
ValueCountFrequency (%) 
041261.5%
 
320.3%
 
520.3%
 
630.4%
 
830.4%
 
920.3%
 
1110.1%
 
1340.6%
 
2520.3%
 
2710.1%
 
ValueCountFrequency (%) 
549510.1%
 
547510.1%
 
546210.1%
 
543410.1%
 
539410.1%
 
536810.1%
 
535020.3%
 
530410.1%
 
528310.1%
 
526810.1%
 

china_pm2.5
Categorical

HIGH CARDINALITY

Distinct count163
Unique (%)24.4%
Missing2
Missing (%)0.3%
Memory size5.4 KiB
117
 
13
98
 
13
87
 
12
99
 
11
105
 
11
Other values (158)
608
ValueCountFrequency (%) 
117131.9%
 
98131.9%
 
87121.8%
 
99111.6%
 
105111.6%
 
106101.5%
 
96101.5%
 
102101.5%
 
122101.5%
 
60101.5%
 
6991.3%
 
8391.3%
 
7191.3%
 
7991.3%
 
10491.3%
 
12191.3%
 
8981.2%
 
10181.2%
 
6381.2%
 
12381.2%
 
9581.2%
 
9381.2%
 
15281.2%
 
11281.2%
 
6681.2%
 
Other values (138)43264.5%
 
2020-11-28T02:38:18.545467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.52238806
Min length1

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
146427.5%
 
91559.2%
 
61559.2%
 
81448.5%
 
71448.5%
 
21337.9%
 
01307.7%
 
51247.3%
 
31197.0%
 
41146.7%
 
n40.2%
 
a20.1%
 
20.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number168299.5%
 
Lowercase Letter60.4%
 
Space Separator20.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n466.7%
 
a233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
146427.6%
 
91559.2%
 
61559.2%
 
81448.6%
 
71448.6%
 
21337.9%
 
01307.7%
 
51247.4%
 
31197.1%
 
41146.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common168499.6%
 
Latin60.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n466.7%
 
a233.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
146427.6%
 
91559.2%
 
61559.2%
 
81448.6%
 
71448.6%
 
21337.9%
 
01307.7%
 
51247.4%
 
31197.1%
 
41146.8%
 
20.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1690100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
146427.5%
 
91559.2%
 
61559.2%
 
81448.5%
 
71448.5%
 
21337.9%
 
01307.7%
 
51247.3%
 
31197.0%
 
41146.7%
 
n40.2%
 
a20.1%
 
20.1%
 

china_pm10
Real number (ℝ≥0)

Distinct count123
Unique (%)18.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean62.63677130044843
Minimum4.0
Maximum197.0
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:18.672155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile25.4
Q146
median59
Q375
95-th percentile109
Maximum197
Range193
Interquartile range (IQR)29

Descriptive statistics

Standard deviation26.0358095
Coefficient of variation (CV)0.415663339
Kurtosis2.362507416
Mean62.6367713
Median Absolute Deviation (MAD)14
Skewness1.066565136
Sum41904
Variance677.8633764
2020-11-28T02:38:18.765879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
56203.0%
 
54172.5%
 
61172.5%
 
64172.5%
 
58162.4%
 
51162.4%
 
62162.4%
 
53152.2%
 
59131.9%
 
49121.8%
 
77121.8%
 
60121.8%
 
57121.8%
 
52111.6%
 
45111.6%
 
69111.6%
 
71111.6%
 
47111.6%
 
63111.6%
 
46111.6%
 
67111.6%
 
65101.5%
 
6691.3%
 
5091.3%
 
4391.3%
 
Other values (98)34952.1%
 
ValueCountFrequency (%) 
410.1%
 
510.1%
 
910.1%
 
1410.1%
 
1520.3%
 
1620.3%
 
1710.1%
 
1840.6%
 
1920.3%
 
2010.1%
 
ValueCountFrequency (%) 
19710.1%
 
16810.1%
 
15930.4%
 
15120.3%
 
15010.1%
 
14810.1%
 
14510.1%
 
14110.1%
 
13510.1%
 
13410.1%
 

china_stock_index
Real number (ℝ≥0)

Distinct count440
Unique (%)65.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2985.5511808669653
Minimum2464.36
Maximum3451.09
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:18.868442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2464.36
5-th percentile2618.23
Q12880.3
median2954.93
Q33090.04
95-th percentile3357.23
Maximum3451.09
Range986.73
Interquartile range (IQR)209.74

Descriptive statistics

Standard deviation201.6272198
Coefficient of variation (CV)0.06753433707
Kurtosis-0.1002067081
Mean2985.551181
Median Absolute Deviation (MAD)94.85
Skewness0.2177738082
Sum1997333.74
Variance40653.53576
2020-11-28T02:38:18.961881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2976.53111.6%
 
2618.23101.5%
 
3218.0591.3%
 
2905.1981.2%
 
3078.3460.9%
 
2860.0860.9%
 
2979.5550.7%
 
2763.9940.6%
 
3031.2440.6%
 
3246.5740.6%
 
2827.840.6%
 
3270.830.4%
 
3338.0930.4%
 
2895.3430.4%
 
2682.3930.4%
 
2912.0130.4%
 
3354.0430.4%
 
3083.7930.4%
 
2919.7430.4%
 
3090.7630.4%
 
3383.3230.4%
 
2954.9330.4%
 
2939.2130.4%
 
2852.3530.4%
 
2891.3430.4%
 
Other values (415)55683.0%
 
ValueCountFrequency (%) 
2464.3610.1%
 
2465.2910.1%
 
2514.8730.4%
 
2526.4610.1%
 
2533.0910.1%
 
2535.110.1%
 
2535.7710.1%
 
2544.3410.1%
 
2553.8330.4%
 
2559.6410.1%
 
ValueCountFrequency (%) 
3451.0910.1%
 
3450.5910.1%
 
3443.2910.1%
 
3438.810.1%
 
3414.6210.1%
 
3410.6110.1%
 
3408.1310.1%
 
3404.810.1%
 
3403.8130.4%
 
3403.4410.1%
 

기온
Real number (ℝ)

Distinct count298
Unique (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.315223880597015
Minimum-8.3
Maximum31.6
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:19.056792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-8.3
5-th percentile-1.7
Q16.3
median15.2
Q323.1
95-th percentile27.1
Maximum31.6
Range39.9
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation9.722824203
Coefficient of variation (CV)0.679194701
Kurtosis-1.13667476
Mean14.31522388
Median Absolute Deviation (MAD)8.4
Skewness-0.2853256216
Sum9591.2
Variance94.53331049
2020-11-28T02:38:19.151968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
24.371.0%
 
2371.0%
 
2571.0%
 
24.260.9%
 
6.860.9%
 
19.660.9%
 
1060.9%
 
24.160.9%
 
26.160.9%
 
24.750.7%
 
9.350.7%
 
13.650.7%
 
6.650.7%
 
22.450.7%
 
23.850.7%
 
23.450.7%
 
-0.150.7%
 
-0.650.7%
 
24.940.6%
 
26.940.6%
 
24.440.6%
 
25.640.6%
 
23.740.6%
 
22.640.6%
 
9.240.6%
 
Other values (273)54080.6%
 
ValueCountFrequency (%) 
-8.310.1%
 
-7.910.1%
 
-6.410.1%
 
-6.110.1%
 
-5.920.3%
 
-5.410.1%
 
-5.210.1%
 
-520.3%
 
-4.910.1%
 
-4.710.1%
 
ValueCountFrequency (%) 
31.610.1%
 
31.510.1%
 
30.510.1%
 
30.420.3%
 
30.210.1%
 
29.910.1%
 
29.810.1%
 
29.710.1%
 
29.410.1%
 
29.210.1%
 

강수량
Real number (ℝ≥0)

ZEROS

Distinct count115
Unique (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6085074626865667
Minimum0.0
Maximum103.1
Zeros479
Zeros (%)71.5%
Memory size5.4 KiB
2020-11-28T02:38:19.256964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3
95-th percentile22.92
Maximum103.1
Range103.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation11.92017304
Coefficient of variation (CV)3.303352749
Kurtosis28.27760287
Mean3.608507463
Median Absolute Deviation (MAD)0
Skewness4.911626206
Sum2417.7
Variance142.0905254
2020-11-28T02:38:19.345996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
047971.5%
 
0.1111.6%
 
0.371.0%
 
0.271.0%
 
0.660.9%
 
0.450.7%
 
1.750.7%
 
3.940.6%
 
0.540.6%
 
2.940.6%
 
140.6%
 
1.830.4%
 
1.130.4%
 
1.330.4%
 
2.230.4%
 
1.230.4%
 
230.4%
 
1.530.4%
 
2.530.4%
 
1.420.3%
 
4.620.3%
 
29.820.3%
 
6.220.3%
 
11.520.3%
 
6.420.3%
 
Other values (90)9814.6%
 
ValueCountFrequency (%) 
047971.5%
 
0.1111.6%
 
0.271.0%
 
0.371.0%
 
0.450.7%
 
0.540.6%
 
0.660.9%
 
0.720.3%
 
0.810.1%
 
140.6%
 
ValueCountFrequency (%) 
103.110.1%
 
102.610.1%
 
9710.1%
 
75.710.1%
 
6910.1%
 
64.710.1%
 
63.210.1%
 
62.310.1%
 
6110.1%
 
6010.1%
 

풍속
Real number (ℝ≥0)

Distinct count38
Unique (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1513432835820896
Minimum0.6
Maximum6.0
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:19.443250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.7
median2
Q32.5
95-th percentile3.455
Maximum6
Range5.4
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.6874856064
Coefficient of variation (CV)0.3195610908
Kurtosis2.302673004
Mean2.151343284
Median Absolute Deviation (MAD)0.4
Skewness1.165202238
Sum1441.4
Variance0.472636459
2020-11-28T02:38:19.535736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2609.0%
 
1.9558.2%
 
1.7558.2%
 
1.6497.3%
 
1.8395.8%
 
2.1375.5%
 
2.3365.4%
 
2.2324.8%
 
2.4294.3%
 
1.5274.0%
 
2.5243.6%
 
2.8213.1%
 
1.2203.0%
 
2.7203.0%
 
1.4192.8%
 
1.3192.8%
 
2.6172.5%
 
3.1131.9%
 
2.9131.9%
 
3121.8%
 
3.391.3%
 
3.471.0%
 
3.271.0%
 
1.171.0%
 
460.9%
 
Other values (13)375.5%
 
ValueCountFrequency (%) 
0.610.1%
 
0.920.3%
 
160.9%
 
1.171.0%
 
1.2203.0%
 
1.3192.8%
 
1.4192.8%
 
1.5274.0%
 
1.6497.3%
 
1.7558.2%
 
ValueCountFrequency (%) 
610.1%
 
4.820.3%
 
4.620.3%
 
4.410.1%
 
4.230.4%
 
460.9%
 
3.930.4%
 
3.820.3%
 
3.750.7%
 
3.640.6%
 

최다풍향
Real number (ℝ≥0)

Distinct count16
Unique (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.955223880597
Minimum20.0
Maximum360.0
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:19.633039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile50
Q170
median270
Q3290
95-th percentile340
Maximum360
Range340
Interquartile range (IQR)220

Descriptive statistics

Standard deviation105.8511566
Coefficient of variation (CV)0.5017707295
Kurtosis-1.242340427
Mean210.9552239
Median Absolute Deviation (MAD)40
Skewness-0.6008689458
Sum141340
Variance11204.46735
2020-11-28T02:38:19.727410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
29017325.8%
 
27012318.4%
 
5010014.9%
 
70558.2%
 
200375.5%
 
320365.4%
 
360274.0%
 
250274.0%
 
230243.6%
 
90223.3%
 
20182.7%
 
34091.3%
 
18060.9%
 
11060.9%
 
16040.6%
 
14030.4%
 
ValueCountFrequency (%) 
20182.7%
 
5010014.9%
 
70558.2%
 
90223.3%
 
11060.9%
 
14030.4%
 
16040.6%
 
18060.9%
 
200375.5%
 
230243.6%
 
ValueCountFrequency (%) 
360274.0%
 
34091.3%
 
320365.4%
 
29017325.8%
 
27012318.4%
 
250274.0%
 
230243.6%
 
200375.5%
 
18060.9%
 
16040.6%
 

습도
Real number (ℝ≥0)

Distinct count377
Unique (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.97179104477612
Minimum17.9
Maximum96.3
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:19.824142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum17.9
5-th percentile34.5
Q147.6
median60
Q371.575
95-th percentile88.71
Maximum96.3
Range78.4
Interquartile range (IQR)23.975

Descriptive statistics

Standard deviation16.22574201
Coefficient of variation (CV)0.2705562353
Kurtosis-0.6218679907
Mean59.97179104
Median Absolute Deviation (MAD)12
Skewness0.09349436219
Sum40181.1
Variance263.2747038
2020-11-28T02:38:19.919932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
48.150.7%
 
68.650.7%
 
46.850.7%
 
51.150.7%
 
6150.7%
 
47.650.7%
 
63.650.7%
 
79.450.7%
 
70.350.7%
 
59.650.7%
 
63.850.7%
 
56.150.7%
 
64.640.6%
 
46.440.6%
 
52.640.6%
 
6340.6%
 
4840.6%
 
63.940.6%
 
64.340.6%
 
70.440.6%
 
40.440.6%
 
69.440.6%
 
5240.6%
 
66.640.6%
 
57.340.6%
 
Other values (352)55883.3%
 
ValueCountFrequency (%) 
17.910.1%
 
24.310.1%
 
25.510.1%
 
25.610.1%
 
26.310.1%
 
26.410.1%
 
26.610.1%
 
27.110.1%
 
27.610.1%
 
28.510.1%
 
ValueCountFrequency (%) 
96.320.3%
 
9610.1%
 
95.810.1%
 
95.510.1%
 
95.410.1%
 
95.310.1%
 
9510.1%
 
94.810.1%
 
94.410.1%
 
93.510.1%
 

기압
Real number (ℝ≥0)

SKEWED

Distinct count277
Unique (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.825671641791
Minimum0.0
Maximum1025.3
Zeros1
Zeros (%)0.1%
Memory size5.4 KiB
2020-11-28T02:38:20.021916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile993.145
Q1999.3
median1004.9
Q31011.375
95-th percentile1018.365
Maximum1025.3
Range1025.3
Interquartile range (IQR)12.075

Descriptive statistics

Standard deviation39.63761479
Coefficient of variation (CV)0.0394865522
Kurtosis617.3912129
Mean1003.825672
Median Absolute Deviation (MAD)6.1
Skewness-24.34635554
Sum672563.2
Variance1571.140506
2020-11-28T02:38:20.108040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
999.381.2%
 
1013.471.0%
 
1004.471.0%
 
1008.760.9%
 
1001.860.9%
 
1000.860.9%
 
998.760.9%
 
1002.260.9%
 
1007.550.7%
 
1001.750.7%
 
995.350.7%
 
1006.850.7%
 
1005.350.7%
 
1010.150.7%
 
1002.450.7%
 
996.450.7%
 
1003.250.7%
 
1004.950.7%
 
999.750.7%
 
998.850.7%
 
1003.450.7%
 
1010.950.7%
 
1003.750.7%
 
998.450.7%
 
1009.150.7%
 
Other values (252)53379.6%
 
ValueCountFrequency (%) 
010.1%
 
983.110.1%
 
983.810.1%
 
984.310.1%
 
985.610.1%
 
987.310.1%
 
987.710.1%
 
988.310.1%
 
988.410.1%
 
989.310.1%
 
ValueCountFrequency (%) 
1025.310.1%
 
102510.1%
 
1024.210.1%
 
1023.710.1%
 
1023.410.1%
 
102320.3%
 
1022.620.3%
 
102220.3%
 
1021.410.1%
 
1021.110.1%
 

안개시간
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count6
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013820895522388061
Minimum0.0
Maximum5.33
Zeros665
Zeros (%)99.3%
Memory size5.4 KiB
2020-11-28T02:38:20.198960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5.33
Range5.33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2202886356
Coefficient of variation (CV)15.93881057
Kurtosis512.0810718
Mean0.01382089552
Median Absolute Deviation (MAD)0
Skewness21.71745376
Sum9.26
Variance0.04852708297
2020-11-28T02:38:20.287851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
066599.3%
 
1.0810.1%
 
0.7810.1%
 
1.410.1%
 
0.6710.1%
 
5.3310.1%
 
ValueCountFrequency (%) 
066599.3%
 
0.6710.1%
 
0.7810.1%
 
1.0810.1%
 
1.410.1%
 
5.3310.1%
 
ValueCountFrequency (%) 
5.3310.1%
 
1.410.1%
 
1.0810.1%
 
0.7810.1%
 
0.6710.1%
 
066599.3%
 

y_pm2.5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count72
Unique (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.435820895522387
Minimum2.0
Maximum135.0
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:20.384891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q113
median20
Q328
95-th percentile49
Maximum135
Range133
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.34889411
Coefficient of variation (CV)0.6841244716
Kurtosis11.45853318
Mean22.4358209
Median Absolute Deviation (MAD)8
Skewness2.512488277
Sum15032
Variance235.5885505
2020-11-28T02:38:20.476955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13314.6%
 
16294.3%
 
23284.2%
 
17274.0%
 
20274.0%
 
10253.7%
 
19243.6%
 
25243.6%
 
22233.4%
 
11223.3%
 
9223.3%
 
21203.0%
 
18203.0%
 
6192.8%
 
15192.8%
 
24182.7%
 
12182.7%
 
28172.5%
 
8162.4%
 
14162.4%
 
26152.2%
 
5142.1%
 
7131.9%
 
29131.9%
 
27131.9%
 
Other values (47)15723.4%
 
ValueCountFrequency (%) 
230.4%
 
381.2%
 
450.7%
 
5142.1%
 
6192.8%
 
7131.9%
 
8162.4%
 
9223.3%
 
10253.7%
 
11223.3%
 
ValueCountFrequency (%) 
13510.1%
 
12910.1%
 
11710.1%
 
10110.1%
 
8510.1%
 
8410.1%
 
8310.1%
 
8110.1%
 
7710.1%
 
7110.1%
 

y_pm10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count97
Unique (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.973134328358206
Minimum5.0
Maximum186.0
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:20.574935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11
Q123
median34
Q347
95-th percentile77.55
Maximum186
Range181
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.40827563
Coefficient of variation (CV)0.5901086657
Kurtosis7.075826012
Mean37.97313433
Median Absolute Deviation (MAD)12
Skewness1.918871313
Sum25442
Variance502.1308168
2020-11-28T02:38:20.673943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
34253.7%
 
39223.3%
 
25223.3%
 
38203.0%
 
22192.8%
 
24192.8%
 
28182.7%
 
14172.5%
 
26172.5%
 
23162.4%
 
43142.1%
 
36142.1%
 
32142.1%
 
37142.1%
 
17142.1%
 
41131.9%
 
20131.9%
 
30131.9%
 
29121.8%
 
47121.8%
 
27111.6%
 
45111.6%
 
33111.6%
 
13101.5%
 
42101.5%
 
Other values (72)28943.1%
 
ValueCountFrequency (%) 
520.3%
 
630.4%
 
740.6%
 
871.0%
 
991.3%
 
1071.0%
 
1130.4%
 
1291.3%
 
13101.5%
 
14172.5%
 
ValueCountFrequency (%) 
18610.1%
 
16910.1%
 
16610.1%
 
14110.1%
 
13410.1%
 
12110.1%
 
12010.1%
 
11510.1%
 
11010.1%
 
10610.1%
 

transport_in
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count669
Unique (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4633766.9701492535
Minimum3056490.0
Maximum5305274.0
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:20.774811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3056490
5-th percentile3703896.55
Q14424019.75
median4760874
Q34936478
95-th percentile5132084.35
Maximum5305274
Range2248784
Interquartile range (IQR)512458.25

Descriptive statistics

Standard deviation439141.0346
Coefficient of variation (CV)0.094769771
Kurtosis1.122757201
Mean4633766.97
Median Absolute Deviation (MAD)230722
Skewness-1.219906232
Sum3104623870
Variance1.928448483e+11
2020-11-28T02:38:20.873220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
447789420.3%
 
507903810.1%
 
479811610.1%
 
504085910.1%
 
443464510.1%
 
490157910.1%
 
432811510.1%
 
440198710.1%
 
366318410.1%
 
519643810.1%
 
507354010.1%
 
472943210.1%
 
462196910.1%
 
511445110.1%
 
441812210.1%
 
462291210.1%
 
448364510.1%
 
486046510.1%
 
505706510.1%
 
509799410.1%
 
485245310.1%
 
495896110.1%
 
441033010.1%
 
494259810.1%
 
429340210.1%
 
Other values (644)64496.1%
 
ValueCountFrequency (%) 
305649010.1%
 
305732910.1%
 
313817810.1%
 
315879710.1%
 
318763810.1%
 
320277710.1%
 
320559410.1%
 
330435010.1%
 
332898610.1%
 
333708610.1%
 
ValueCountFrequency (%) 
530527410.1%
 
527863910.1%
 
527330810.1%
 
524152610.1%
 
522791610.1%
 
522141810.1%
 
521406910.1%
 
521049410.1%
 
520974910.1%
 
520736410.1%
 

transport_out
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count670
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4673159.323880597
Minimum3060106.0
Maximum5302127.0
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:20.973958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3060106
5-th percentile3676536.15
Q14492052.5
median4813010.5
Q34992929.75
95-th percentile5144803.35
Maximum5302127
Range2242021
Interquartile range (IQR)500877.25

Descriptive statistics

Standard deviation454200.3204
Coefficient of variation (CV)0.09719341647
Kurtosis1.221700328
Mean4673159.324
Median Absolute Deviation (MAD)208309.5
Skewness-1.320565763
Sum3131016747
Variance2.06297931e+11
2020-11-28T02:38:21.074866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
462881810.1%
 
461899110.1%
 
387416410.1%
 
397879810.1%
 
489575710.1%
 
437720610.1%
 
411785910.1%
 
508998310.1%
 
505720210.1%
 
506538010.1%
 
480058710.1%
 
379834310.1%
 
377722110.1%
 
455743010.1%
 
319621310.1%
 
504073210.1%
 
491783210.1%
 
477037310.1%
 
473984010.1%
 
441832010.1%
 
517200110.1%
 
513050010.1%
 
492625410.1%
 
472170910.1%
 
472988910.1%
 
Other values (645)64596.3%
 
ValueCountFrequency (%) 
306010610.1%
 
309148010.1%
 
313076910.1%
 
314203910.1%
 
314541110.1%
 
319314610.1%
 
319621310.1%
 
326907210.1%
 
330895210.1%
 
333383710.1%
 
ValueCountFrequency (%) 
530212710.1%
 
530012510.1%
 
527613910.1%
 
524702610.1%
 
524156010.1%
 
524095110.1%
 
524055310.1%
 
522523210.1%
 
522039610.1%
 
521218710.1%
 

transport_inandout
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count670
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9306926.29402985
Minimum6116596.0
Maximum10528041.0
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:21.175873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6116596
5-th percentile7357709.4
Q18920641.25
median9612497.5
Q39932750
95-th percentile10246614
Maximum10528041
Range4411445
Interquartile range (IQR)1012108.75

Descriptive statistics

Standard deviation885463.786
Coefficient of variation (CV)0.09514030282
Kurtosis1.243739328
Mean9306926.294
Median Absolute Deviation (MAD)396329.5
Skewness-1.309594845
Sum6235640617
Variance7.840461163e+11
2020-11-28T02:38:21.268570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
774143610.1%
 
998339010.1%
 
939156210.1%
 
666282310.1%
 
967199310.1%
 
893030410.1%
 
788002210.1%
 
985212110.1%
 
985208210.1%
 
1016337010.1%
 
871287010.1%
 
998311910.1%
 
946592210.1%
 
958984010.1%
 
954066910.1%
 
1006494110.1%
 
996005510.1%
 
926605410.1%
 
999933510.1%
 
860710710.1%
 
639874010.1%
 
1022867810.1%
 
973767510.1%
 
780996810.1%
 
1013128810.1%
 
Other values (645)64596.3%
 
ValueCountFrequency (%) 
611659610.1%
 
614880910.1%
 
628358910.1%
 
630083610.1%
 
631840710.1%
 
639874010.1%
 
639899010.1%
 
660615810.1%
 
661330210.1%
 
666282310.1%
 
ValueCountFrequency (%) 
1052804110.1%
 
1052567010.1%
 
1050387110.1%
 
1048318010.1%
 
1045070010.1%
 
1042759010.1%
 
1042625610.1%
 
1041620010.1%
 
1040041310.1%
 
1039056110.1%
 

china_covid_confirmed
Real number (ℝ≥0)

ZEROS

Distinct count150
Unique (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.26716417910447
Minimum0.0
Maximum15141.0
Zeros381
Zeros (%)56.9%
Memory size5.4 KiB
2020-11-28T02:38:21.361315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326
95-th percentile402.8
Maximum15141
Range15141
Interquartile range (IQR)26

Descriptive statistics

Standard deviation753.1475053
Coefficient of variation (CV)5.526991846
Kurtosis240.5314885
Mean136.2671642
Median Absolute Deviation (MAD)0
Skewness13.29325204
Sum91299
Variance567231.1647
2020-11-28T02:38:21.463057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
038156.9%
 
281.2%
 
1981.2%
 
2281.2%
 
1771.0%
 
1571.0%
 
2960.9%
 
360.9%
 
2360.9%
 
1860.9%
 
3360.9%
 
2060.9%
 
1450.7%
 
550.7%
 
750.7%
 
1250.7%
 
2450.7%
 
2550.7%
 
2740.6%
 
640.6%
 
940.6%
 
1140.6%
 
3640.6%
 
3230.4%
 
3430.4%
 
Other values (125)15923.7%
 
ValueCountFrequency (%) 
038156.9%
 
130.4%
 
281.2%
 
360.9%
 
430.4%
 
550.7%
 
640.6%
 
750.7%
 
820.3%
 
940.6%
 
ValueCountFrequency (%) 
1514110.1%
 
415610.1%
 
387210.1%
 
372710.1%
 
341810.1%
 
323710.1%
 
316010.1%
 
297410.1%
 
281210.1%
 
260710.1%
 

china_covid_death
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count57
Unique (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.073134328358209
Minimum0.0
Maximum1290.0
Zeros530
Zeros (%)79.1%
Memory size5.4 KiB
2020-11-28T02:38:21.565876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile31.55
Maximum1290
Range1290
Interquartile range (IQR)0

Descriptive statistics

Standard deviation53.97427024
Coefficient of variation (CV)7.630884377
Kurtosis479.5038506
Mean7.073134328
Median Absolute Deviation (MAD)0
Skewness20.48147298
Sum4739
Variance2913.221848
2020-11-28T02:38:21.658873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
053079.1%
 
1345.1%
 
2172.5%
 
3131.9%
 
471.0%
 
650.7%
 
940.6%
 
530.4%
 
1530.4%
 
1320.3%
 
9720.3%
 
9820.3%
 
720.3%
 
2520.3%
 
1120.3%
 
14210.1%
 
7310.1%
 
25410.1%
 
10810.1%
 
8910.1%
 
8610.1%
 
4310.1%
 
7210.1%
 
6610.1%
 
6510.1%
 
Other values (32)324.8%
 
ValueCountFrequency (%) 
053079.1%
 
1345.1%
 
2172.5%
 
3131.9%
 
471.0%
 
530.4%
 
650.7%
 
720.3%
 
810.1%
 
940.6%
 
ValueCountFrequency (%) 
129010.1%
 
25410.1%
 
15010.1%
 
14310.1%
 
14210.1%
 
13910.1%
 
11810.1%
 
11210.1%
 
10910.1%
 
10810.1%
 

kospi_index
Real number (ℝ≥0)

Distinct count451
Unique (%)67.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2121.008355754858
Minimum1457.64
Maximum2443.58
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-11-28T02:38:21.754941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1457.64
5-th percentile1860.7
Q12049.2
median2130.62
Q32204.21
95-th percentile2368.892
Maximum2443.58
Range985.94
Interquartile range (IQR)155.01

Descriptive statistics

Standard deviation154.6194775
Coefficient of variation (CV)0.07289904214
Kurtosis1.640133463
Mean2121.008356
Median Absolute Deviation (MAD)75.91
Skewness-0.7253265253
Sum1418954.59
Variance23907.18283
2020-11-28T02:38:21.843867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2203.4660.9%
 
2327.8960.9%
 
2049.250.7%
 
2246.1350.7%
 
1947.5650.7%
 
2407.4940.6%
 
2196.3240.6%
 
2195.4440.6%
 
2391.9640.6%
 
1889.0130.4%
 
2125.6230.4%
 
2396.6930.4%
 
2209.6130.4%
 
2041.7430.4%
 
2206.3930.4%
 
2200.4430.4%
 
2010.2530.4%
 
1937.7530.4%
 
1970.1330.4%
 
2250.5730.4%
 
2170.2530.4%
 
2029.630.4%
 
2368.2530.4%
 
2075.5730.4%
 
2087.9630.4%
 
Other values (426)57886.3%
 
ValueCountFrequency (%) 
1457.6410.1%
 
1482.4610.1%
 
1566.1530.4%
 
1591.210.1%
 
1609.9710.1%
 
1672.4410.1%
 
1685.4610.1%
 
1686.2410.1%
 
1704.7610.1%
 
1714.8610.1%
 
ValueCountFrequency (%) 
2443.5810.1%
 
2437.5310.1%
 
2435.9210.1%
 
2432.3510.1%
 
2427.9110.1%
 
2418.6710.1%
 
2412.430.4%
 
2407.4940.6%
 
2406.1710.1%
 
2403.7310.1%
 
Distinct count22
Unique (%)3.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
4,603
 
60
3,674
 
31
4,010
 
31
4,094
 
31
4,011
 
31
Other values (17)
486
ValueCountFrequency (%) 
4,603609.0%
 
3,674314.6%
 
4,010314.6%
 
4,094314.6%
 
4,011314.6%
 
5,462314.6%
 
3,188314.6%
 
4,426314.6%
 
4,632314.6%
 
3,046314.6%
 
2,982314.6%
 
5,008314.6%
 
3,224314.6%
 
4,208304.5%
 
3,738304.5%
 
3,590304.5%
 
5,117304.5%
 
2,979304.5%
 
4,804304.5%
 
3,833294.3%
 
3,116284.2%
 
3,63310.1%
 
2020-11-28T02:38:21.963546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
,67020.0%
 
449114.7%
 
342312.6%
 
036711.0%
 
22778.3%
 
62447.3%
 
82437.3%
 
12407.2%
 
91524.5%
 
51223.6%
 
71213.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number268080.0%
 
Other Punctuation67020.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
449118.3%
 
342315.8%
 
036713.7%
 
227710.3%
 
62449.1%
 
82439.1%
 
12409.0%
 
91525.7%
 
51224.6%
 
71214.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,670100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3350100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
,67020.0%
 
449114.7%
 
342312.6%
 
036711.0%
 
22778.3%
 
62447.3%
 
82437.3%
 
12407.2%
 
91524.5%
 
51223.6%
 
71213.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3350100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
,67020.0%
 
449114.7%
 
342312.6%
 
036711.0%
 
22778.3%
 
62447.3%
 
82437.3%
 
12407.2%
 
91524.5%
 
51223.6%
 
71213.6%
 

Interactions

2020-11-28T02:37:17.952723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:18.155252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:18.290534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:18.427512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:18.592579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:18.731210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:18.871859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:19.012486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:19.166046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:19.364080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:19.517928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:19.663539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:19.802204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:19.987674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:20.166198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:20.301835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:20.434480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:20.566152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:20.724736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:20.896316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:21.030956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:21.177563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:21.330135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:21.477777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:21.625384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:21.773295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:21.954838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:22.122414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:22.247116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:22.471065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:22.642627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:22.777303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:22.912552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:23.059206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:23.228514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:23.425941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:23.594494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:23.744633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:23.896244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:24.038424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:24.172149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:24.324793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:24.520240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:24.649895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:24.781193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:24.905207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:25.068088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:25.225179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:25.390770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:25.519392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:25.654031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:25.776703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:25.912141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:26.035934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:26.163593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:26.285448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:26.414435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:26.545871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:26.738312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:26.913848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:27.055974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:27.232663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:27.414528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:27.584415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:27.872442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:28.041114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:28.203335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:28.339402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:28.495182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:28.673218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:28.857323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:29.043844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:29.177458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:29.316124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:29.459886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:29.626842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:29.762575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:29.897624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:30.039421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:30.200056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:30.399039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:30.530686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:30.665883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:30.795099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:30.940019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:31.068075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:31.203081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:31.328066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:31.449255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:31.573386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:31.703091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:31.828047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:31.948087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:32.074964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:32.194032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:32.311298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:32.434040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:32.555077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:32.684082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:32.811128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:32.931011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:33.075228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:33.220962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:33.361071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:33.540109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:33.691737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:33.835317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:33.969957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:34.219291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:34.391468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:34.576969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:34.710120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:34.843763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:34.984531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:35.143109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:35.276753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:35.409371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:35.572662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:35.780209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:35.921349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:36.046016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:36.165696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:36.306321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:36.431984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:36.559642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:36.683333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:36.829099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:36.981725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:37.145259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:37.271307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:37.400003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:37.523284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:37.656882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:37.783052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:37.913702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:38.052332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:38.207458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:38.363012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:38.514184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:38.661019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:38.782843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:38.925555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:39.056206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:39.179882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:39.317672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:39.463284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:39.654744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:39.775208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:39.894756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:40.015740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:40.139755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:40.275785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:40.398835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:40.526093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:40.654717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:37:40.775908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-11-28T02:38:10.917322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:11.050112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:11.187975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:11.318028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:11.455008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:11.582977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:11.712950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:11.843960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:11.978021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:12.106081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:12.227278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:12.358882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:12.491890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:12.619920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:12.747183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:12.874913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:13.012011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:13.144877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:13.271251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:13.398046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:13.557156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:13.691823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:13.820064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:13.940715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:14.061417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:14.172121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:14.286930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:14.409957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:14.538558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:14.659195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:14.778013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:14.902861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:15.029949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:15.149950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:15.272829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:15.393444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:15.521901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:15.646321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-11-28T02:38:22.091901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-28T02:38:22.367971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-28T02:38:22.645966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-28T02:38:22.919084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-28T02:38:15.937080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:16.427868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:16.657245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-28T02:38:16.807462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

dateseoul_covid_confirmedseoul_covid_deathseoul_covid_releasedchina_pm2.5china_pm10china_stock_index기온강수량풍속최다풍향습도기압안개시간y_pm2.5y_pm10transport_intransport_outtransport_inandoutchina_covid_confirmedchina_covid_deathkospi_indexsoeul_oil_consumption
02019-01-010.00.00.0NaNNaNNaN-5.00.02.1290.049.51022.60.025.038.03534195.03531605.07065800.00.00.0NaN3,633
12019-01-020.00.00.0NaN125.02465.29-4.90.01.7270.042.81023.40.022.034.04658330.04578983.09237313.00.00.02010.002,982
22019-01-030.00.00.0182133.02464.36-3.50.01.4290.038.81024.20.024.039.04892831.04780205.09673036.00.00.01993.702,982
32019-01-040.00.00.0198151.02514.87-1.10.01.220.055.51019.50.042.060.05011360.04946281.09957641.00.00.02010.252,982
42019-01-050.00.00.022464.02514.87-2.80.02.2290.040.31016.60.040.063.04434645.04382017.08816662.00.00.02010.252,982
52019-01-060.00.00.010691.02514.87-2.80.01.2290.035.01017.60.023.049.03635917.03525408.07161325.00.00.02010.252,982
62019-01-070.00.00.0161159.02533.09-1.90.01.4290.046.41017.40.037.057.04625330.04623444.09248774.00.00.02037.102,982
72019-01-080.00.00.021964.02526.46-3.50.02.6290.031.01018.20.018.041.04928774.04935362.09864136.00.00.02025.272,982
82019-01-090.00.00.07963.02544.34-4.70.01.350.028.81022.00.022.051.04891248.04900404.09791652.00.00.02064.712,982
92019-01-100.00.00.09374.02535.10-0.60.01.3290.048.81015.50.034.058.04956516.04939590.09896106.00.00.02063.282,982

Last rows

dateseoul_covid_confirmedseoul_covid_deathseoul_covid_releasedchina_pm2.5china_pm10china_stock_index기온강수량풍속최다풍향습도기압안개시간y_pm2.5y_pm10transport_intransport_outtransport_inandoutchina_covid_confirmedchina_covid_deathkospi_indexsoeul_oil_consumption
6602020-10-225748.074.05283.0132105.03312.5013.50.02.850.066.01003.50.030.063.04618706.04825112.09443818.022.00.02355.054,603
6612020-10-235768.074.05304.08352.03278.008.60.03.0270.051.51004.90.09.025.04692848.04940239.09633087.029.00.02360.814,603
6622020-10-245790.074.05350.05454.03278.008.70.02.5270.047.61008.70.010.025.04503569.04729889.09233458.033.00.02360.814,603
6632020-10-255807.074.05350.06367.03278.0011.80.01.720.062.51009.90.015.029.03903108.04062970.07966078.019.00.02360.814,603
6642020-10-265827.074.05368.010692.03251.1212.90.01.620.071.61009.80.028.050.04423630.04717361.09140991.026.00.02343.914,603
6652020-10-275851.076.05394.014498.03254.3214.00.01.8270.074.11010.90.047.074.04530392.04774614.09305006.024.00.02330.844,603
6662020-10-285876.077.05434.012669.03269.2413.80.02.5270.052.11012.60.027.049.04588788.04836718.09425506.042.00.02345.264,603
6672020-10-295912.078.05462.08892.03272.7310.80.02.0270.040.31013.70.015.037.04623029.04886518.09509547.024.00.02326.674,603
6682020-10-305960.078.05475.012793.03224.5311.90.01.550.042.91015.20.019.043.04695985.04958248.09654233.025.00.02267.154,603
6692020-10-316011.078.05495.015277.03224.5313.60.02.6200.060.31013.90.014.028.04523453.04787495.09310948.033.00.02267.154,603